1. Proceedings
  2. I3M
  3. 2022
  4. SESDE
  5. 10.46354/i3m.2022.sesde.005

Agent-based modelling of a small-scale fishery in Corsica

  • a Eric Innocenti , 
  • b Corinne Idda, 
  • c Dominique Prunetti, 
  • d Pierre-Régis Gonsolin
  • a,b,cUMR CNRS 6240 LISA, University of Corsica - Pasquale Paoli; Campus Mariani, Bât. Edmond Simeoni, BP52, 20250 Corte, France
  • d Corsica Institute of Technology, University of Corsica - Pasquale Paoli; Campus Grimaldi, BP52, 20250 Corte, France
Cite as
Innocenti E., Idda C., Prunetti D., and Gonsolin P.R. (2022).,Agent-based modelling of a small-scale fishery in Corsica. Proceedings of the 10th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2022). , 005 . DOI: https://doi.org/10.46354/i3m.2022.sesde.005

Abstract

In this work we introduce a new multi-stock, multi-fleet, multi-species and bioeconomic model for the complex system of a small-scale fishery. The objective is to study fisheries in order to ensure the renewal of the stock of biomass. This stock represents both a means of subsistence for fishermen but also contributes to food security. We model the system as a Multi-Agent System using both Cellular Automata Model (CAM) and Agent-Based Model (ABM) computational modelling approaches. CAM are used to describe the environment and the dynamics of resources. ABM are used to describe the behaviour of fishing activities. The main interest of the conceptual model lies in the proposed laws and in its capacity to organize hierarchically all the local interactions and transition rules within the simulated entities. We report preliminary results showing that our modelling approach facilitates software parameterization for the specific requirements implied by the context of a small-scale fishery. The main results of this work consist in the creation of a computer modelling structure CAM and ABM, which constitutes a preliminary for an optimized resources management. In a future development, we will improve the behavior of economic agents in order to consider the complexity of their decision making.

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